Sequence-aware Pre-training for Echocardiography Probe Guidance

• URL: http://arxiv.org/abs/2408.15026v1
• Date: Tue, 27 Aug 2024 12:55:54 GMT
• Title: Sequence-aware Pre-training for Echocardiography Probe Guidance
• Authors: Haojun Jiang, Zhenguo Sun, Yu Sun, Ning Jia, Meng Li, Shaqi Luo, Shiji Song, Gao Huang,
• Abstract summary: Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features. We propose a sequence-aware self-supervised pre-training method to learn personalized 2D and 3D cardiac structural features.
• Score: 66.35766658717205
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cardiac ultrasound probe guidance aims to help novices adjust the 6-DOF probe pose to obtain high-quality sectional images. Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features, leading to a performance bottleneck. Clinically, we observed that sonographers adjust their understanding of a patient's cardiac structure based on prior scanning sequences, thereby modifying their scanning strategies. Inspired by this, we propose a sequence-aware self-supervised pre-training method. Specifically, our approach learns personalized 2D and 3D cardiac structural features by predicting the masked-out images and actions in a scanning sequence. We hypothesize that if the model can predict the missing content it has acquired a good understanding of the personalized cardiac structure. In the downstream probe guidance task, we also introduced a sequence modeling approach that models individual cardiac structural information based on the images and actions from historical scan data, enabling more accurate navigation decisions. Experiments on a large-scale dataset with 1.36 million samples demonstrated that our proposed sequence-aware paradigm can significantly reduce navigation errors, with translation errors decreasing by 15.90% to 36.87% and rotation errors decreasing by 11.13% to 20.77%, compared to state-of-the-art methods.

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